R Notebook: estimation of gas costs using the measure marginal method; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_marginal approach for a single given env =
ethereumjs.
PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.
Expected a result file ethereumjs_pg_marginal_full5_c50_step1_shuffle_50_4.csv.
programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count sample_id run_id measure_total_time_ns env program_id
## 1 ADD 27 0 0 326491 ethereumjs ADD_27
## 2 ADD 27 0 1 322832 ethereumjs ADD_27
## 3 ADD 27 0 2 328756 ethereumjs ADD_27
## 4 ADD 27 0 3 322898 ethereumjs ADD_27
## 5 ADD 27 0 4 326553 ethereumjs ADD_27
## 6 ADD 27 0 5 329741 ethereumjs ADD_27Switch removed_outliers to FALSE to see the
comparison.
boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
if (removed_outliers) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}
# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
mix_model = normalmixEM(df$measure_total_time_ns)
print(summary(mix_model))
plot(mix_model,which=2)
mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
over_threshold = which(df$measure_total_time_ns > mode_midpoint)
df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
return(df)
}
# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
if (missing(bimodal_opcodes)) {
bimodal_opcodes = c()
}
if (missing(plots)) {
plots = "scatter"
}
if (missing(use_median)) {
use_median = FALSE
}
print(c(opcode, env))
df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
if (opcode %in% bimodal_opcodes) {
par(mfrow=c(1,2))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
title(main=paste(env, opcode))
# correct_bimodal plots the second plot inside
df = correct_bimodal(df)
}
if (use_median) {
f = median
} else {
f = mean
}
df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
print(summary(model_mean))
slope = model_mean$coefficients[['op_count']]
stderr = summary(model_mean)$coefficients['op_count','Std. Error']
if (plots == "scatter" | plots == "all") {
par(mfrow=c(1,1))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
rounded_slope = round(slope, 3)
rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
rounded_stderr = round(stderr, 3)
title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
abline(model_mean, col="red")
}
if (plots == "diagnostics" | plots == "all") {
par(mfrow=c(2,2))
plot(model_mean)
}
list("slope" = slope, "stderr" = stderr)
}
extract_opcodes <- function() {
unique(measurements$opcode)
}all_opcodes = extract_opcodes()
# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.
for (opcode in all_opcodes) {
boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}
Now we can investigate the linear regressions.
if (env == 'evmone') {
bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
bimodals = c()
}
for (opcode in all_opcodes) {
estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}## [1] "ADD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1809.0 -766.0 -71.5 437.6 4018.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 312033.34 329.18 947.91 <0.0000000000000002 ***
## op_count 359.33 11.35 31.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1193 on 49 degrees of freedom
## Multiple R-squared: 0.9534, Adjusted R-squared: 0.9525
## F-statistic: 1003 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MUL" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1878.2 -821.2 -210.6 512.8 2596.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 312184.78 322.38 968.37 <0.0000000000000002 ***
## op_count 383.13 11.11 34.48 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1168 on 49 degrees of freedom
## Multiple R-squared: 0.9604, Adjusted R-squared: 0.9596
## F-statistic: 1189 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SUB" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2604.6 -1044.1 -466.8 704.7 3973.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 311678.59 435.62 715.49 <0.0000000000000002 ***
## op_count 363.12 15.02 24.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1578 on 49 degrees of freedom
## Multiple R-squared: 0.9227, Adjusted R-squared: 0.9211
## F-statistic: 584.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DIV" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2459.9 -825.8 -105.2 645.4 3561.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315163.8 345.2 913.03 <0.0000000000000002 ***
## op_count 309.4 11.9 26.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1251 on 49 degrees of freedom
## Multiple R-squared: 0.9324, Adjusted R-squared: 0.9311
## F-statistic: 676.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SDIV" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2583.9 -886.4 -169.5 1000.6 4067.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 318632.90 427.39 745.54 <0.0000000000000002 ***
## op_count 237.01 14.73 16.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1549 on 49 degrees of freedom
## Multiple R-squared: 0.8408, Adjusted R-squared: 0.8376
## F-statistic: 258.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MOD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2813.5 -963.4 54.0 670.4 4095.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 311621.5 391.6 795.83 <0.0000000000000002 ***
## op_count 373.2 13.5 27.65 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1419 on 49 degrees of freedom
## Multiple R-squared: 0.9398, Adjusted R-squared: 0.9385
## F-statistic: 764.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SMOD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2069.1 -864.4 341.0 728.1 1834.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 314952.97 292.86 1075.45 <0.0000000000000002 ***
## op_count 313.79 10.09 31.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1061 on 49 degrees of freedom
## Multiple R-squared: 0.9517, Adjusted R-squared: 0.9508
## F-statistic: 966.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDMOD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2244.6 -873.1 -232.9 896.6 6026.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 309736.33 388.00 798.28 <0.0000000000000002 ***
## op_count 402.04 13.37 30.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1406 on 49 degrees of freedom
## Multiple R-squared: 0.9486, Adjusted R-squared: 0.9475
## F-statistic: 903.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MULMOD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1983.3 -893.8 -125.0 913.1 3617.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 309676.65 344.31 899.41 <0.0000000000000002 ***
## op_count 401.34 11.87 33.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1248 on 49 degrees of freedom
## Multiple R-squared: 0.9589, Adjusted R-squared: 0.9581
## F-statistic: 1144 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EXP" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4522.4 -1037.4 -229.6 426.8 5302.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315096.51 579.30 543.92 <0.0000000000000002 ***
## op_count 1923.39 19.97 96.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2099 on 49 degrees of freedom
## Multiple R-squared: 0.9947, Adjusted R-squared: 0.9946
## F-statistic: 9278 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SIGNEXTEND" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1611.82 -648.29 -41.95 538.48 2869.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 314480.701 269.549 1166.7 <0.0000000000000002 ***
## op_count 476.584 9.291 51.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 976.7 on 49 degrees of freedom
## Multiple R-squared: 0.9817, Adjusted R-squared: 0.9813
## F-statistic: 2631 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "LT" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4744.5 -1982.5 -204.4 1576.4 6198.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 307579.96 708.14 434.35 <0.0000000000000002 ***
## op_count 397.28 24.41 16.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2566 on 49 degrees of freedom
## Multiple R-squared: 0.8439, Adjusted R-squared: 0.8407
## F-statistic: 264.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GT" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4593.6 -2036.0 -435.2 1764.5 5378.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 307921.35 719.02 428.25 <0.0000000000000002 ***
## op_count 397.65 24.78 16.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2605 on 49 degrees of freedom
## Multiple R-squared: 0.8401, Adjusted R-squared: 0.8368
## F-statistic: 257.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SLT" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1906.37 -684.92 -11.52 625.82 3160.85
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 313918.149 277.146 1132.68 <0.0000000000000002 ***
## op_count 287.914 9.553 30.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1004 on 49 degrees of freedom
## Multiple R-squared: 0.9488, Adjusted R-squared: 0.9478
## F-statistic: 908.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SGT" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2225.66 -861.95 -93.68 943.46 2332.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 313112.70 318.41 983.4 <0.0000000000000002 ***
## op_count 306.20 10.98 27.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1154 on 49 degrees of freedom
## Multiple R-squared: 0.9408, Adjusted R-squared: 0.9396
## F-statistic: 778.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EQ" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4118.9 -1946.5 -272.6 1615.2 5502.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 307830.9 699.2 440.2 <0.0000000000000002 ***
## op_count 407.4 24.1 16.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2534 on 49 degrees of freedom
## Multiple R-squared: 0.8536, Adjusted R-squared: 0.8506
## F-statistic: 285.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ISZERO" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1406.92 -544.50 6.88 479.72 1274.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 314264.802 196.926 1595.85 <0.0000000000000002 ***
## op_count 217.023 6.788 31.97 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 713.5 on 49 degrees of freedom
## Multiple R-squared: 0.9543, Adjusted R-squared: 0.9533
## F-statistic: 1022 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "AND" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1941.7 -1150.1 -200.0 641.7 4372.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 310730.90 406.87 763.71 <0.0000000000000002 ***
## op_count 313.31 14.02 22.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1474 on 49 degrees of freedom
## Multiple R-squared: 0.9106, Adjusted R-squared: 0.9088
## F-statistic: 499.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "OR" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1631.2 -922.3 -415.6 729.0 3603.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 310833.0 345.1 900.61 <0.0000000000000002 ***
## op_count 299.9 11.9 25.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1251 on 49 degrees of freedom
## Multiple R-squared: 0.9284, Adjusted R-squared: 0.927
## F-statistic: 635.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "XOR" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2261.7 -928.7 -125.2 700.4 4609.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 310739.34 404.71 767.81 <0.0000000000000002 ***
## op_count 311.73 13.95 22.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1466 on 49 degrees of freedom
## Multiple R-squared: 0.9106, Adjusted R-squared: 0.9088
## F-statistic: 499.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NOT" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1605.6 -617.1 -125.7 360.7 4061.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 310825.995 268.879 1156.01 <0.0000000000000002 ***
## op_count 275.016 9.268 29.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 974.2 on 49 degrees of freedom
## Multiple R-squared: 0.9473, Adjusted R-squared: 0.9462
## F-statistic: 880.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "BYTE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1805.0 -892.5 -364.5 382.5 4148.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 311334.6 342.5 909.07 <0.0000000000000002 ***
## op_count 432.5 11.8 36.64 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1241 on 49 degrees of freedom
## Multiple R-squared: 0.9648, Adjusted R-squared: 0.9641
## F-statistic: 1342 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHL" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1784.9 -670.2 -259.6 605.9 3485.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 310698.98 328.21 946.66 <0.0000000000000002 ***
## op_count 359.97 11.31 31.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1189 on 49 degrees of freedom
## Multiple R-squared: 0.9538, Adjusted R-squared: 0.9529
## F-statistic: 1012 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHR" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2795.3 -1340.2 510.6 1154.0 2644.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 318133.97 411.19 773.69 <0.0000000000000002 ***
## op_count 216.36 14.17 15.27 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1490 on 49 degrees of freedom
## Multiple R-squared: 0.8263, Adjusted R-squared: 0.8227
## F-statistic: 233 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SAR" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2623.5 -888.5 55.2 1024.5 3246.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 313910.93 369.95 848.52 <0.0000000000000002 ***
## op_count 243.92 12.75 19.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1340 on 49 degrees of freedom
## Multiple R-squared: 0.8819, Adjusted R-squared: 0.8795
## F-statistic: 365.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDRESS" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1465.88 -211.77 85.65 269.29 958.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103371.555 135.957 760.3 <0.0000000000000002 ***
## op_count 485.237 4.686 103.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 492.6 on 49 degrees of freedom
## Multiple R-squared: 0.9955, Adjusted R-squared: 0.9954
## F-statistic: 1.072e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ORIGIN" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1720.05 -237.06 -10.85 323.70 1244.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103442.0 145.1 713.06 <0.0000000000000002 ***
## op_count 481.3 5.0 96.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 525.6 on 49 degrees of freedom
## Multiple R-squared: 0.9947, Adjusted R-squared: 0.9946
## F-statistic: 9265 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLER" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1275.69 -275.23 11.05 294.78 846.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103584.686 134.155 772.1 <0.0000000000000002 ***
## op_count 481.307 4.624 104.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 486.1 on 49 degrees of freedom
## Multiple R-squared: 0.9955, Adjusted R-squared: 0.9954
## F-statistic: 1.083e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLVALUE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1060.01 -183.31 -18.32 199.71 889.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103026.368 107.900 954.84 <0.0000000000000002 ***
## op_count 254.599 3.719 68.45 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 391 on 49 degrees of freedom
## Multiple R-squared: 0.9897, Adjusted R-squared: 0.9894
## F-statistic: 4686 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATALOAD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6531 -1137 483 1296 2746
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 327736.8 539.7 607.28 <0.0000000000000002 ***
## op_count 261.6 18.6 14.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1955 on 49 degrees of freedom
## Multiple R-squared: 0.8015, Adjusted R-squared: 0.7974
## F-statistic: 197.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATASIZE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1672.00 -270.44 -38.44 376.27 1131.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103628.997 150.500 688.57 <0.0000000000000002 ***
## op_count 246.498 5.188 47.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 545.3 on 49 degrees of freedom
## Multiple R-squared: 0.9788, Adjusted R-squared: 0.9783
## F-statistic: 2258 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATACOPY" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3202.2 -556.2 109.1 612.4 2519.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 248710.44 376.42 660.7 <0.0000000000000002 ***
## op_count 2174.46 12.97 167.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1364 on 49 degrees of freedom
## Multiple R-squared: 0.9983, Adjusted R-squared: 0.9982
## F-statistic: 2.809e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODESIZE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -946.5 -313.8 -94.8 315.3 1182.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101376.242 139.357 727.46 <0.0000000000000002 ***
## op_count 262.240 4.803 54.59 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 504.9 on 49 degrees of freedom
## Multiple R-squared: 0.9838, Adjusted R-squared: 0.9835
## F-statistic: 2980 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODECOPY" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14449.0 -5060.3 76.1 4290.9 21881.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1155511.48 1943.38 594.59 <0.0000000000000002 ***
## op_count 1867.56 66.99 27.88 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7042 on 49 degrees of freedom
## Multiple R-squared: 0.9407, Adjusted R-squared: 0.9395
## F-statistic: 777.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASPRICE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1633.65 -214.82 29.72 385.30 928.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102298.961 141.059 725.22 <0.0000000000000002 ***
## op_count 225.682 4.862 46.42 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 511.1 on 49 degrees of freedom
## Multiple R-squared: 0.9778, Adjusted R-squared: 0.9773
## F-statistic: 2154 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATASIZE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1556.25 -596.50 6.49 487.19 2775.59
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103529.755 219.700 471.23 <0.0000000000000002 ***
## op_count 240.880 7.573 31.81 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 796.1 on 49 degrees of freedom
## Multiple R-squared: 0.9538, Adjusted R-squared: 0.9529
## F-statistic: 1012 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATACOPY" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2996.4 -1091.2 -58.5 538.3 4737.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 395577.18 486.08 813.8 <0.0000000000000002 ***
## op_count 2167.89 16.75 129.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1761 on 49 degrees of freedom
## Multiple R-squared: 0.9971, Adjusted R-squared: 0.997
## F-statistic: 1.674e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "COINBASE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1352.06 -344.85 0.15 428.02 980.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102849.116 156.730 656.2 <0.0000000000000002 ***
## op_count 833.809 5.402 154.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 567.9 on 49 degrees of freedom
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9979
## F-statistic: 2.382e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "TIMESTAMP" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1450.4 -483.5 -111.4 545.3 1803.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103201.243 217.418 474.67 <0.0000000000000002 ***
## op_count 243.848 7.494 32.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 787.8 on 49 degrees of freedom
## Multiple R-squared: 0.9558, Adjusted R-squared: 0.9549
## F-statistic: 1059 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NUMBER" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1297.8 -573.8 -18.5 479.3 1688.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103380.841 203.439 508.17 <0.0000000000000002 ***
## op_count 246.087 7.012 35.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 737.1 on 49 degrees of freedom
## Multiple R-squared: 0.9617, Adjusted R-squared: 0.961
## F-statistic: 1232 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DIFFICULTY" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1699.97 -435.90 -11.74 473.71 1288.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103280.36 191.76 538.6 <0.0000000000000002 ***
## op_count 1400.27 6.61 211.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 694.8 on 49 degrees of freedom
## Multiple R-squared: 0.9989, Adjusted R-squared: 0.9989
## F-statistic: 4.488e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASLIMIT" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1164.9 -520.8 -200.1 600.0 2666.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103306.401 220.820 467.83 <0.0000000000000002 ***
## op_count 249.226 7.611 32.74 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 800.1 on 49 degrees of freedom
## Multiple R-squared: 0.9563, Adjusted R-squared: 0.9554
## F-statistic: 1072 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CHAINID" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1389.4 -719.5 113.5 524.0 2342.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102957.393 225.882 455.80 <0.0000000000000002 ***
## op_count 212.122 7.786 27.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 818.5 on 49 degrees of freedom
## Multiple R-squared: 0.9381, Adjusted R-squared: 0.9368
## F-statistic: 742.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SELFBALANCE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1080.09 -219.00 65.48 295.36 761.06
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102336.453 125.377 816.23 <0.0000000000000002 ***
## op_count 217.779 4.322 50.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 454.3 on 49 degrees of freedom
## Multiple R-squared: 0.9811, Adjusted R-squared: 0.9807
## F-statistic: 2539 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "POP" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1337.14 -363.16 -23.17 498.08 1209.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236808.140 168.323 1406.87 <0.0000000000000002 ***
## op_count 109.501 5.802 18.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 609.9 on 49 degrees of freedom
## Multiple R-squared: 0.8791, Adjusted R-squared: 0.8766
## F-statistic: 356.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MLOAD" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3071.5 -1211.3 -14.6 1241.2 2594.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 328909.30 422.12 779.18 <0.0000000000000002 ***
## op_count 880.59 14.55 60.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1530 on 49 degrees of freedom
## Multiple R-squared: 0.9868, Adjusted R-squared: 0.9865
## F-statistic: 3663 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2099.1 -969.3 -115.4 418.7 7669.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 245021.13 497.41 492.6 <0.0000000000000002 ***
## op_count 2256.06 17.15 131.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1802 on 49 degrees of freedom
## Multiple R-squared: 0.9972, Adjusted R-squared: 0.9971
## F-statistic: 1.731e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE8" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2940.7 -924.1 -209.1 703.2 3356.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 244767.40 389.05 629.15 <0.0000000000000002 ***
## op_count 821.15 13.41 61.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1410 on 49 degrees of freedom
## Multiple R-squared: 0.9871, Adjusted R-squared: 0.9868
## F-statistic: 3750 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMP" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1194.46 -286.54 52.67 340.08 1026.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 111055.205 146.415 758.49 <0.0000000000000002 ***
## op_count 184.778 5.047 36.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 530.5 on 49 degrees of freedom
## Multiple R-squared: 0.9647, Adjusted R-squared: 0.964
## F-statistic: 1341 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPI" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3859.9 -1123.6 279.7 1019.9 3421.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320239.38 433.42 738.87 <0.0000000000000002 ***
## op_count 465.23 14.94 31.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1570 on 49 degrees of freedom
## Multiple R-squared: 0.9519, Adjusted R-squared: 0.9509
## F-statistic: 969.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PC" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -732.39 -213.89 -30.66 228.47 823.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101762.468 103.819 980.19 <0.0000000000000002 ***
## op_count 236.455 3.579 66.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 376.2 on 49 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9887
## F-statistic: 4366 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSIZE" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1110.81 -262.49 14.45 290.62 949.17
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101636.470 131.775 771.29 <0.0000000000000002 ***
## op_count 243.726 4.542 53.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 477.5 on 49 degrees of freedom
## Multiple R-squared: 0.9833, Adjusted R-squared: 0.9829
## F-statistic: 2879 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GAS" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1305.23 -257.02 -51.49 265.79 1122.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101944.792 119.081 856.09 <0.0000000000000002 ***
## op_count 223.097 4.105 54.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 431.5 on 49 degrees of freedom
## Multiple R-squared: 0.9837, Adjusted R-squared: 0.9834
## F-statistic: 2954 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPDEST" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -283.39 -51.79 18.91 71.61 174.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20572.987 29.253 703.3 <0.0000000000000002 ***
## op_count 176.300 1.008 174.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 106 on 49 degrees of freedom
## Multiple R-squared: 0.9984, Adjusted R-squared: 0.9984
## F-statistic: 3.057e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH1" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1650.4 -610.9 -213.9 480.4 2111.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103374.136 231.314 446.9 <0.0000000000000002 ***
## op_count 1165.825 7.973 146.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 838.1 on 49 degrees of freedom
## Multiple R-squared: 0.9977, Adjusted R-squared: 0.9977
## F-statistic: 2.138e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH2" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1822.87 -575.89 -82.61 420.50 1929.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102871.325 226.791 453.6 <0.0000000000000002 ***
## op_count 1223.821 7.817 156.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 821.7 on 49 degrees of freedom
## Multiple R-squared: 0.998, Adjusted R-squared: 0.998
## F-statistic: 2.451e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH3" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1962.06 -641.70 -64.03 344.10 2322.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102772.626 248.960 412.8 <0.0000000000000002 ***
## op_count 1280.351 8.581 149.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 902.1 on 49 degrees of freedom
## Multiple R-squared: 0.9978, Adjusted R-squared: 0.9978
## F-statistic: 2.226e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH4" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1871.72 -675.65 82.48 345.96 2699.74
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102701.020 269.907 380.5 <0.0000000000000002 ***
## op_count 1321.616 9.303 142.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 978 on 49 degrees of freedom
## Multiple R-squared: 0.9976, Adjusted R-squared: 0.9975
## F-statistic: 2.018e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH5" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1340.02 -512.57 -17.99 377.02 1748.62
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102789.689 197.744 519.8 <0.0000000000000002 ***
## op_count 1366.061 6.816 200.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 716.5 on 49 degrees of freedom
## Multiple R-squared: 0.9988, Adjusted R-squared: 0.9988
## F-statistic: 4.017e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH6" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -880.82 -491.96 -79.26 480.24 1213.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103032.657 165.965 620.8 <0.0000000000000002 ***
## op_count 1419.880 5.721 248.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 601.4 on 49 degrees of freedom
## Multiple R-squared: 0.9992, Adjusted R-squared: 0.9992
## F-statistic: 6.16e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH7" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1398.51 -403.76 -21.45 291.94 1339.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102916.677 162.370 633.8 <0.0000000000000002 ***
## op_count 1460.043 5.597 260.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 588.3 on 49 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 6.805e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH8" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1043.3 -360.7 -10.0 273.0 1524.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102966.106 138.262 744.7 <0.0000000000000002 ***
## op_count 1495.699 4.766 313.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 501 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 9.85e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH9" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1783.97 -432.26 -90.72 465.71 1477.83
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102606.458 196.673 521.7 <0.0000000000000002 ***
## op_count 1544.550 6.779 227.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 712.6 on 49 degrees of freedom
## Multiple R-squared: 0.9991, Adjusted R-squared: 0.999
## F-statistic: 5.191e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH10" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1887.41 -360.78 -57.88 346.45 1676.07
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102702.872 187.653 547.3 <0.0000000000000002 ***
## op_count 1578.120 6.468 244.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 679.9 on 49 degrees of freedom
## Multiple R-squared: 0.9992, Adjusted R-squared: 0.9992
## F-statistic: 5.953e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH11" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1008.9 -489.5 -17.6 361.0 1292.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102819.027 154.085 667.3 <0.0000000000000002 ***
## op_count 1611.984 5.311 303.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 558.3 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 9.212e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH12" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1360.85 -542.56 81.22 434.17 1619.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102677.674 196.720 521.9 <0.0000000000000002 ***
## op_count 1651.329 6.781 243.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 712.8 on 49 degrees of freedom
## Multiple R-squared: 0.9992, Adjusted R-squared: 0.9992
## F-statistic: 5.931e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH13" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1268.65 -513.69 -31.75 473.41 1441.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102530.74 185.37 553.1 <0.0000000000000002 ***
## op_count 1689.93 6.39 264.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 671.7 on 49 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 6.995e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH14" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1659.1 -341.9 -7.8 351.6 1421.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102777.895 165.817 619.8 <0.0000000000000002 ***
## op_count 1731.311 5.716 302.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 600.8 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 9.176e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH15" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1089.43 -412.80 -78.74 415.06 1273.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102564.817 158.184 648.4 <0.0000000000000002 ***
## op_count 1770.989 5.452 324.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 573.2 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 1.055e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH16" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -966.50 -466.30 -64.15 326.10 1329.93
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102802.305 161.453 636.7 <0.0000000000000002 ***
## op_count 1816.071 5.565 326.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 585 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 1.065e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH17" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -943.55 -285.23 -18.69 369.83 1153.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102893.477 130.383 789.2 <0.0000000000000002 ***
## op_count 1853.906 4.494 412.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 472.4 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.702e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH18" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1207.75 -468.79 35.23 303.83 1262.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102715.088 150.041 684.6 <0.0000000000000002 ***
## op_count 1898.360 5.172 367.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 543.7 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.347e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH19" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1284.73 -426.84 -6.73 288.24 1633.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102829.406 184.904 556.1 <0.0000000000000002 ***
## op_count 1929.140 6.373 302.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 670 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 9.162e+04 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH20" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1306.17 -331.63 40.15 372.58 1413.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102664.429 148.778 690.0 <0.0000000000000002 ***
## op_count 1978.026 5.128 385.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 539.1 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.488e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH21" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1176.92 -385.71 67.59 275.65 1455.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102733.778 153.085 671.1 <0.0000000000000002 ***
## op_count 2013.705 5.277 381.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 554.7 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.456e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH22" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1051.2 -376.2 -56.9 289.4 1746.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102807.486 172.571 595.7 <0.0000000000000002 ***
## op_count 2060.493 5.948 346.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 625.3 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.2e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH23" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1258.84 -396.32 13.04 370.52 1399.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102726.195 170.164 603.7 <0.0000000000000002 ***
## op_count 2088.540 5.865 356.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 616.6 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.268e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH24" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1028.43 -446.35 -59.22 253.87 1664.75
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102941.774 159.126 646.9 <0.0000000000000002 ***
## op_count 2134.363 5.485 389.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 576.6 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.514e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH25" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1553.80 -442.94 40.19 365.23 1576.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102782.558 188.356 545.7 <0.0000000000000002 ***
## op_count 2165.911 6.492 333.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 682.5 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.113e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH26" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1672.89 -363.24 86.77 375.31 1628.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102664.954 180.287 569.5 <0.0000000000000002 ***
## op_count 2218.256 6.214 357.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 653.2 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.274e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH27" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1035.08 -435.32 -86.96 240.28 2274.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103022.4 200.2 514.7 <0.0000000000000002 ***
## op_count 2244.6 6.9 325.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 725.3 on 49 degrees of freedom
## Multiple R-squared: 0.9995, Adjusted R-squared: 0.9995
## F-statistic: 1.058e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH28" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1951.2 -316.2 96.4 298.0 1205.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102913.149 178.257 577.3 <0.0000000000000002 ***
## op_count 2272.573 6.144 369.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 645.9 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.368e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH29" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1596.55 -400.29 -49.39 399.77 1506.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102859.517 191.272 537.8 <0.0000000000000002 ***
## op_count 2305.738 6.593 349.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 693 on 49 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 1.223e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH30" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1232.67 -364.61 70.57 443.41 1429.37
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 102982.182 158.738 648.8 <0.0000000000000002 ***
## op_count 2342.760 5.472 428.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 575.2 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.833e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH31" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1145.8 -349.8 10.7 342.4 1072.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103182.322 153.519 672.1 <0.0000000000000002 ***
## op_count 2390.967 5.292 451.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 556.3 on 49 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998
## F-statistic: 2.042e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH32" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1517.65 -326.95 57.05 379.86 1127.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103330.149 162.159 637.2 <0.0000000000000002 ***
## op_count 2417.828 5.589 432.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 587.6 on 49 degrees of freedom
## Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
## F-statistic: 1.871e+05 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP1" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4128 -1624 -276 1203 5897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315462.39 568.29 555.11 <0.0000000000000002 ***
## op_count 307.38 19.59 15.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2059 on 49 degrees of freedom
## Multiple R-squared: 0.834, Adjusted R-squared: 0.8306
## F-statistic: 246.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP2" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3404 -1439 -145 1058 5726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315798.26 547.97 576.31 <0.0000000000000002 ***
## op_count 296.70 18.89 15.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1985 on 49 degrees of freedom
## Multiple R-squared: 0.8343, Adjusted R-squared: 0.8309
## F-statistic: 246.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP3" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4142.7 -1279.9 -185.9 954.4 5457.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315922.54 593.13 532.64 <0.0000000000000002 ***
## op_count 295.52 20.44 14.46 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2149 on 49 degrees of freedom
## Multiple R-squared: 0.81, Adjusted R-squared: 0.8062
## F-statistic: 208.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP4" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5775.5 -1348.7 -188.8 1151.0 6858.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315922.50 595.80 530.25 <0.0000000000000002 ***
## op_count 292.82 20.54 14.26 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2159 on 49 degrees of freedom
## Multiple R-squared: 0.8058, Adjusted R-squared: 0.8018
## F-statistic: 203.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP5" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4051.8 -1109.9 -511.8 575.2 6524.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315306.05 611.06 516.00 <0.0000000000000002 ***
## op_count 316.47 21.06 15.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2214 on 49 degrees of freedom
## Multiple R-squared: 0.8217, Adjusted R-squared: 0.818
## F-statistic: 225.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP6" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4816.3 -1734.5 -192.2 1032.7 7406.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315609.94 659.17 478.80 <0.0000000000000002 ***
## op_count 288.40 22.72 12.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2388 on 49 degrees of freedom
## Multiple R-squared: 0.7668, Adjusted R-squared: 0.762
## F-statistic: 161.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP7" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2599.0 -568.2 82.9 644.2 3635.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 318991.65 302.80 1053.46 <0.0000000000000002 ***
## op_count 236.61 10.44 22.67 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1097 on 49 degrees of freedom
## Multiple R-squared: 0.9129, Adjusted R-squared: 0.9112
## F-statistic: 513.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP8" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5373.8 -548.2 254.1 632.6 3316.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 321765.83 365.09 881.34 <0.0000000000000002 ***
## op_count 216.75 12.58 17.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1323 on 49 degrees of freedom
## Multiple R-squared: 0.8582, Adjusted R-squared: 0.8554
## F-statistic: 296.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP9" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3256.2 -1134.6 -505.6 809.9 5600.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315431.02 531.95 592.97 <0.0000000000000002 ***
## op_count 303.31 18.34 16.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1927 on 49 degrees of freedom
## Multiple R-squared: 0.8481, Adjusted R-squared: 0.845
## F-statistic: 273.6 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP10" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4097.2 -1010.6 -364.3 759.1 5928.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 316224.02 506.68 624.11 <0.0000000000000002 ***
## op_count 283.39 17.46 16.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1836 on 49 degrees of freedom
## Multiple R-squared: 0.8431, Adjusted R-squared: 0.8399
## F-statistic: 263.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP11" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4234.5 -1662.1 -485.4 1945.6 5927.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 320439.70 678.46 472.30 <0.0000000000000002 ***
## op_count 287.84 23.39 12.31 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2458 on 49 degrees of freedom
## Multiple R-squared: 0.7556, Adjusted R-squared: 0.7506
## F-statistic: 151.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP12" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4015.2 -1387.5 -126.2 685.1 6303.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315688.77 555.91 567.88 <0.0000000000000002 ***
## op_count 307.43 19.16 16.04 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2014 on 49 degrees of freedom
## Multiple R-squared: 0.8401, Adjusted R-squared: 0.8368
## F-statistic: 257.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP13" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3197.3 -1257.5 -354.1 494.8 7549.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 318701.19 562.61 566.47 <0.0000000000000002 ***
## op_count 281.56 19.39 14.52 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2039 on 49 degrees of freedom
## Multiple R-squared: 0.8114, Adjusted R-squared: 0.8075
## F-statistic: 210.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP14" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3551.8 -666.1 -33.3 491.4 4108.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 319651.26 311.89 1024.90 <0.0000000000000002 ***
## op_count 208.42 10.75 19.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1130 on 49 degrees of freedom
## Multiple R-squared: 0.8847, Adjusted R-squared: 0.8823
## F-statistic: 375.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP15" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3428.6 -1135.8 -205.7 1041.3 3644.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 315415.75 454.87 693.41 <0.0000000000000002 ***
## op_count 318.89 15.68 20.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1648 on 49 degrees of freedom
## Multiple R-squared: 0.8941, Adjusted R-squared: 0.8919
## F-statistic: 413.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "DUP16" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4537.5 -1151.9 -541.2 1072.0 5019.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 325763.5 638.2 510.40 <0.0000000000000002 ***
## op_count 269.0 22.0 12.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2313 on 49 degrees of freedom
## Multiple R-squared: 0.7531, Adjusted R-squared: 0.7481
## F-statistic: 149.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP1" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -962.64 -318.43 -8.43 263.01 1295.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234398.589 137.548 1704.12 <0.0000000000000002 ***
## op_count 145.184 4.741 30.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 498.4 on 49 degrees of freedom
## Multiple R-squared: 0.9503, Adjusted R-squared: 0.9493
## F-statistic: 937.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP2" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1418.78 -270.21 23.12 365.77 1178.55
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234163.448 141.435 1655.62 <0.0000000000000002 ***
## op_count 146.052 4.875 29.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 512.5 on 49 degrees of freedom
## Multiple R-squared: 0.9482, Adjusted R-squared: 0.9472
## F-statistic: 897.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP3" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1166.29 -323.68 -11.37 338.28 1193.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234124.914 156.494 1496.06 <0.0000000000000002 ***
## op_count 153.692 5.394 28.49 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 567 on 49 degrees of freedom
## Multiple R-squared: 0.9431, Adjusted R-squared: 0.9419
## F-statistic: 811.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP4" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -792.92 -394.66 -15.05 329.49 1035.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234288.364 126.454 1852.76 <0.0000000000000002 ***
## op_count 155.836 4.359 35.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 458.2 on 49 degrees of freedom
## Multiple R-squared: 0.9631, Adjusted R-squared: 0.9623
## F-statistic: 1278 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP5" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -859.01 -303.52 -59.54 254.12 1228.95
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234324.551 117.032 2002.22 <0.0000000000000002 ***
## op_count 151.230 4.034 37.49 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 424 on 49 degrees of freedom
## Multiple R-squared: 0.9663, Adjusted R-squared: 0.9656
## F-statistic: 1405 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP6" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -966.88 -305.56 21.25 351.79 1231.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236870.440 131.876 1796.16 <0.0000000000000002 ***
## op_count 144.270 4.546 31.74 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 477.8 on 49 degrees of freedom
## Multiple R-squared: 0.9536, Adjusted R-squared: 0.9527
## F-statistic: 1007 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP7" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -995.08 -332.44 15.99 192.14 1263.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 239101.451 136.305 1754.17 <0.0000000000000002 ***
## op_count 140.142 4.698 29.83 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 493.9 on 49 degrees of freedom
## Multiple R-squared: 0.9478, Adjusted R-squared: 0.9467
## F-statistic: 889.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP8" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1488.08 -311.83 -61.08 395.92 1643.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234256.579 165.986 1411.31 <0.0000000000000002 ***
## op_count 145.167 5.721 25.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 601.4 on 49 degrees of freedom
## Multiple R-squared: 0.9293, Adjusted R-squared: 0.9278
## F-statistic: 643.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP9" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -799.22 -264.58 -19.26 250.19 1279.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234459.51 114.89 2040.71 <0.0000000000000002 ***
## op_count 150.54 3.96 38.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 416.3 on 49 degrees of freedom
## Multiple R-squared: 0.9672, Adjusted R-squared: 0.9665
## F-statistic: 1445 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP10" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1044.91 -426.16 9.95 368.81 2006.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 242419.743 165.864 1461.56 <0.0000000000000002 ***
## op_count 147.551 5.717 25.81 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 601 on 49 degrees of freedom
## Multiple R-squared: 0.9315, Adjusted R-squared: 0.9301
## F-statistic: 666.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP11" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1128.78 -292.75 47.57 263.98 1358.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234400.192 132.469 1769.47 <0.0000000000000002 ***
## op_count 140.941 4.566 30.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 480 on 49 degrees of freedom
## Multiple R-squared: 0.9511, Adjusted R-squared: 0.9501
## F-statistic: 952.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP12" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1269.87 -343.70 45.05 384.73 1413.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236867.98 156.95 1509.2 <0.0000000000000002 ***
## op_count 137.95 5.41 25.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 568.7 on 49 degrees of freedom
## Multiple R-squared: 0.9299, Adjusted R-squared: 0.9285
## F-statistic: 650.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP13" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1159.59 -305.16 -66.35 300.04 1357.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236674.908 163.373 1448.68 <0.0000000000000002 ***
## op_count 143.839 5.631 25.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 592 on 49 degrees of freedom
## Multiple R-squared: 0.9301, Adjusted R-squared: 0.9287
## F-statistic: 652.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP14" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1287.10 -337.03 1.29 403.53 1316.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 234149.557 161.145 1453.03 <0.0000000000000002 ***
## op_count 145.826 5.555 26.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 583.9 on 49 degrees of freedom
## Multiple R-squared: 0.9336, Adjusted R-squared: 0.9323
## F-statistic: 689.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP15" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1205.1 -446.1 106.8 476.2 1589.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 247972.985 192.111 1290.78 <0.0000000000000002 ***
## op_count 148.410 6.622 22.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 696.1 on 49 degrees of freedom
## Multiple R-squared: 0.9111, Adjusted R-squared: 0.9093
## F-statistic: 502.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SWAP16" "ethereumjs"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1290.20 -390.01 84.99 420.15 1299.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 242344.583 157.426 1539.42 <0.0000000000000002 ***
## op_count 143.317 5.426 26.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 570.4 on 49 degrees of freedom
## Multiple R-squared: 0.9344, Adjusted R-squared: 0.933
## F-statistic: 697.6 on 1 and 49 DF, p-value: < 0.00000000000000022
Export the results
write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)